Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
multi-class-classification
Languages:
Arabic
Size:
100K - 1M
License:
File size: 3,536 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Hotel Reviews in Arabic language"""
import os
import datasets
from datasets.tasks import TextClassification
_DESCRIPTION = """\
This dataset contains 93700 hotel reviews in Arabic language.\
The hotel reviews were collected from Booking.com website during June/July 2016.\
The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.\
The following table summarize some tatistics on the HARD Dataset.
"""
_CITATION = """\
@incollection{elnagar2018hotel,
title={Hotel Arabic-reviews dataset construction for sentiment analysis applications},
author={Elnagar, Ashraf and Khalifa, Yasmin S and Einea, Anas},
booktitle={Intelligent Natural Language Processing: Trends and Applications},
pages={35--52},
year={2018},
publisher={Springer}
}
"""
_DOWNLOAD_URL = "https://raw.githubusercontent.com/elnagara/HARD-Arabic-Dataset/master/data/balanced-reviews.zip"
class HardConfig(datasets.BuilderConfig):
"""BuilderConfig for Hard."""
def __init__(self, **kwargs):
"""BuilderConfig for Hard.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(HardConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class Hard(datasets.GeneratorBasedBuilder):
"""Hard dataset."""
BUILDER_CONFIGS = [
HardConfig(
name="plain_text",
description="Plain text",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(
names=[
"1",
"2",
"3",
"4",
"5",
]
),
}
),
supervised_keys=None,
homepage="https://github.com/elnagara/HARD-Arabic-Dataset",
citation=_CITATION,
task_templates=[TextClassification(text_column="text", label_column="label")],
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"directory": os.path.join(data_dir, "balanced-reviews.txt")}
),
]
def _generate_examples(self, directory):
"""Generate examples."""
with open(directory, mode="r", encoding="utf-16") as file:
for id_, line in enumerate(file.read().splitlines()[1:]):
_, _, rating, _, _, _, review_text = line.split("\t")
yield str(id_), {"text": review_text, "label": rating}
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